About

Engineering depth, with an expanding scope.

I am a senior software engineer with more than six years of experience across backend development, Linux-based systems, telecom software, automation, CI/CD and cloud-native delivery.

I built my foundations in signal processing, C/C++ and Linux, then broadened that work into Python, reusable backend capabilities, delivery automation, containers and platform tooling.

I care about maintainable systems, useful developer tooling, reliability and using AI thoughtfully across implementation, testing and review.

That progression expands my engineering scope without leaving systems thinking behind. The same attention to failure modes, interfaces and operational evidence informs my backend and delivery work today.

Side projects give me a practical space to deepen those skills by designing APIs, exercising incident scenarios, automating reports and evaluating newer AI-assisted workflows.

Academic foundations

Signal processing, communications and practical experimentation.

Earlier academic projects underpin the systems thinking used in my professional engineering work. They are presented as thesis work, not current featured applications.

2015–2020 · Sweden

M.Sc. Electrical Engineering — Signal Processing

Blekinge Institute of Technology (BTH)

Thesis

Development of a Multi Sensor Android Application

Developed two native Android applications using Java, XML and the Android SDK. One detected available smartphone sensors and visualised their measurements; the other used calibrated proximity and accelerometer input for lock-screen interaction. Both were tested with Genymotion and physical Android devices, producing the expected sensor outputs, graphs and lock-screen behaviour.

JavaAndroid SDKAndroid StudioXMLSmartphone sensorsAccelerometerProximity sensorSensor visualisationGenymotionDevice testing

2011–2015 · India

B.Tech. Electronics and Communication Engineering

Jawaharlal Nehru Technological University, Kakinada

Thesis

Channel Estimation of OFDM by LS and MMSE Methods

Implemented and simulated OFDM channel-estimation techniques in MATLAB, comparing LS and MMSE estimators under fading and noisy wireless-channel conditions. Evaluation used MSE and BER/SER across SNR levels: MMSE produced lower estimation and error rates, while LS offered substantially lower complexity and hardware requirements and remained suitable at higher SNR.

MATLABOFDMWireless communicationsChannel estimationLS and MMSEBPSKRayleigh fadingSNRMSEBER / SERSignal processing

Beyond code